PhD Theses at KOM

Tim Dutz

Monday November 28, 2016

English abstract:

Extensive cohort studies show that physical inactivity is likely to have negative consequences for one’s health. The World Health Organization thus recommends a minimum of thirty minutes of medium-intensity physical activity per day, an amount that can easily be reached by doing some brisk walking or leisure cycling. Recently, a Taiwanese-American team of scientists was able to prove that even less effort is required for positive health effects and that as little as fifteen minutes of physical activity per day will increase one’s life expectancy by up to three years on the average. However, simply spreading this knowledge is not sufficient. Roughly one in three Europeans and US-Americans does not even meet the minimum recommendations for physical activity, although the majority of these people is aware of the damage that their behavior may do to their health. And this ‘willful wrongdoing’ does not only concern individuals: Due to the large number of inactive people, the problem of sedentary behavior affects societies as a whole, not the least by increasing public health costs.But if it is not a lack of knowledge that causes this problem, what is? And what can be done to stimulate leisure-time physical activity? The Fogg Behavior Model (FBM), developed by psychologist and Stanford-lecturer B.J. Fogg, explains the factors that determine whether or not a given person will show a desired behavior. The core components of the FBM include a trigger that can be perceived by the target person and that she associates with the desired behavior, as well as her ability and motivation for this behavior at the time when the trigger reaches her. If the combined amount of ability and motivation exceeds a lower limit, the so-called activation threshold, then the triggered person will behave in the desired way; otherwise, she will not. Based on the understanding of human behavior that the FBM conveys, this thesis focuses on the question of how mobile devices can assist people in reaching the minimum amount of daily physical activity that is required for health benefits.An in-depth analysis of the problem reveals that of the three possible strategies - trying to increase a user’s ability for leisure-time physical activity, trying to increase her motivation for the same, and trying to increase her short-term awareness for its necessity and feasibility through triggers – the creation of adaptive triggers is the most promising approach. This task in turn consists of several sub-problems, such as the problem of how to recognize the user’s current contextual situation, the problem of how to decide, whether or not the recognized situation is suited for an activation attempt, and the problem of interacting with the user in those cases in which an activation attempt seems worthwhile. Learning from the user’s behavior and understanding her preferences and constraints is the key factor in the creation of accurate and reliable intervention mechanisms. To this end, smartphone sensors, wearables, and Web services are utilized for collecting information about the state of the user and her environment. This data is then analyzed by a supervised learning machine which, based on prior experience, estimates the probability for a successful activation attempt in the current situation. Ideally, the learner will identify a kairotic moment: A situation, in which a trigger is bound to initiate the desired behavior. If it does, it reaches out to the user.Multiple types of such triggering mechanisms were embedded into the mobile exergame ‘Twostone’, an application that requires brisk walking or easy running from its users. During a field study with thirty participants, the performances of these different approaches were compared against one another. The study revealed a surprising result: Not the most-knowledgeable intervention mechanism emerged as a winner, but it was rather the triggering variant that relied on a reduced number of contextual information to achieve both the highest triggering success rates and the best user acceptance. The study also showed that intervention mechanisms can indeed increase the prevalence of a desired behavior, but only if the user has a positive attitude towards the respective activity. As such, both the conceptual model for technology-based interventive measures and the evaluation results that are presented in this thesis offer valuable insights for developers of devices and applications that aim to foster desired behaviors in general and increased levels of daily physical activity in particular.